• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.179-201
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• 2004

Online traffic flow modeling is attracting more attention due to intelligent transport systems and technologies. The flow-density relation plays an important role in traffic flow modeling and provides a basic way to illustrate traffic flow behavior under different traffic flow and traffic density conditions. Until now the research effort has focused mainly on the shape of the relation. The time series of the relation has not been identified clearly, even though the time series of the relation reflects the upstream/downstream traffic conditions and should be considered in the traffic flow modeling. In this paper the flow-density relation is analyzed dynamically and interpreted as a states diagram. The dynamic flow-density relation is quantified by applying fuzzy logic. The quantified dynamic flow-density relation builds the basis for online application of a macroscopic traffic flow model. The new approach to online modeling of traffic flow applying the dynamic flow-density relation alleviates parameter calibration problems stemming from the static flow-density relation.

• International Journal of Highway Engineering
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• v.17 no.6
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• pp.75-83
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• 2015

PURPOSES : This study deals with the traffic accidents classified by the traffic analysis zone. The purpose is to develop the accident density models by using zonal traffic and socioeconomic data. METHODS : The traffic accident density models are developed through multiple linear regression analysis. In this study, three multiple linear models were developed. The dependent variable was traffic accident density, which is a measure of the relative distribution of traffic accidents. The independent variables were various traffic and socioeconomic variables. CONCLUSIONS : Three traffic accident density models were developed, and all models were statistically significant. Road length, trip production volume, intersections, van ratio, and number of vehicles per person in the transportation-based model were analyzed to be positive to the accident. Residential and commercial area ratio and transportation vulnerability ratio obtained using the socioeconomic-based model were found to affect the accident. The major arterial road ratio, trip production volume, intersection, van ratio, commercial ratio, and number of companies in the integrated model were also found to be related to the accident.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.13
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• pp.5229-5232
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• 2014

Background: While many studies have concluded that local traffic density is positively associated with childhood leukemia, the results are inconsistent. We therefore performed a meta-analysis to assess the relationship between traffic density and the risk of childhood leukemia. Methods: A systematic literature review was carried out using PubMed, EMBASE, and the Cochrane Library from January 1979 to December 2013. We selected and assessed journal articles evaluating the relationship between local traffic density and the risk of leukemia in children. The analysis was carried out using STATA version 12.0. Results: A total of 11 articles, including 12 estimates of effect, were included in our meta-analysis. The summary effect size from the random-effects model, expressed as an odds ratio, was 1.03 (95% CI: 0.98-1.09, p=0.002). No significant association between traffic density and the risk of childhood leukemia was found. Similar conclusions were found on subgroup analysis. Conclusions: The results of our meta-analysis suggested no association between traffic density and the risk of childhood leukemia. This implies that living in close proximity to roads with heavy traffic may not increase the risk of childhood leukemia. However, further high-quality prospective trials are needed to support these results.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.77-87
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• 2018

Traffic Density is the most important of the three primary macroscopic traffic stream parameters, because it is most directly related to traffic demand(Traffic Engineering, 2004). It is defined as the number of existing vehicles within a given distance at a certain time. However, due to weather, road conditions, and cost issues, collecting density directly on the field is difficult. This makes studies of density less actively than those of traffic volume or velocity. For these reasons, there is insufficient attempts on divers collecting methods or researches on the accuracy of measured values. In this paper, we used the 'Density Measuring System' based on the synthesise technology of several camera images as a method to measure density. The collected density value by the 'Density Mesuring System' is selected as the true value based on the density define, and this value was compared with the density calculated by the traditional measurement methods. As a result of the comparison, the density value using the fundamental equation method is the closest to the true value as RMSE shows 1.8 to 2.5. In addition, we investigated some issues that can be overlooked easily such as the collecting interval to be considered on collecting density directly by calculating the moment density and the average density. Despite the actual traffic situation of the experiment site is LOS B, it is difficult to judge the real traffic situation because the moment density values per second are observed max 16.0 (veh/km) to min 2.0 (veh/km). However, the average density measured for 15 minutes at 30-second intervals was 8.3-7.9 (veh/km) and it indicates precisely LOS B.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.208-221
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• 2020

The objective of this study is to estimate and analyze the traffic density of continuous flow using the trajectory of individual vehicles and the headway of sample probe vehicles-front vehicles obtained from ADAS (Advanced Driver Assitance System) installed in sample probe vehicles. In the past, traffic density of continuous traffic flow was mainly estimated by processing data such as traffic volume, speed, and share collected from Vehicle Detection System, or by counting the number of vehicles directly using video information such as CCTV. This method showed the limitation of spatial limitations in estimating traffic density, and low reliability of estimation in the event of traffic congestion. To overcome the limitations of prior research, In this study, individual vehicle trajectory data and vehicle headway information collected from ADAS are used to detect the space on the road and to estimate the spatiotemporal traffic density using the Generalized Density formula. As a result, an analysis of the accuracy of the traffic density estimates according to the sampling rate of ADAS vehicles showed that the expected sampling rate of 30% was approximately 90% consistent with the actual traffic density. This study contribute to efficient traffic operation management by estimating reliable traffic density in road situations where ADAS and autonomous vehicles are mixed.

• Journal of Korean Society of Transportation
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• v.24 no.5 s.91
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• pp.135-142
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• 2006

The flow-density relations represent equilibrium relations between flow and density in the stationary state. Using individual vehicle data this paper proposed a method to 131ter traffic data in the stationary state and showed flow-density relations produced by the traffic data in the stationary state. The Proposed method is based on the idea that free flow and congested flow show totally different traffic behaviors and time series of the traffic data observed at detection stations. The traffic data collected from the stationary state in the free flow using this filtering method consist in the left branch of the flow-density relation and the traffic data collected from the stationary state in the congested flow consist in the right branch of the flow-density relation. The traffic data in the stationary state skew reproducible flow-density relation in the almost whole range of the traffic flow.

• International Journal of Highway Engineering
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• v.18 no.5
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• pp.117-125
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• 2016

PURPOSES : The density in uninterrupted traffic flow facilities plays an important role in representing the current status of traffic flow. For example, the density is used for the primary measures of effectiveness in the capacity analysis for freeway facilities. Therefore, the estimation of density has been a long and tough task for traffic engineers for a long time. This study was initiated to evaluate the performance of density values that were estimated using VDS data and two traditional methods, including a method using traffic flow theory and another method using occupancy by comparing the density values estimated using vehicular trajectory data generated from a radar detector. METHODS : In this study, a radar detector which can generate very accurate vehicular trajectory within the range of 250 m on the Joongbu expressway near to Dongseoul tollgate, where two VDS were already installed. The first task was to estimate densities using different data and methods. Thus, the density values were estimated using two traditional methods and the VDS data on the Joongbu expressway. The density values were compared with those estimated using the vehicular trajectory data in order to evaluate the quality of density estimation. Then, the relationship between the space mean speed and density were drawn using two sets of densities and speeds based on the VDS data and one set of those using the radar detector data. CONCLUSIONS : As a result, the three sets of density showed minor differences when the density values were under 20 vehicles per km per lane. However, as the density values become greater than 20 vehicles per km per lane, the three methods showed a significant difference among on another. The density using the vehicular trajectory data showed the lowest values in general. Based on the in-depth study, it was found out that the space mean speed plays a critical role in the calculation of density. The speed estimated from the VDS data was higher than that from the radar detector. In order to validate the difference in the speed data, the traffic flow models using the relationships between the space mean speed and the density were carefully examined in this study. Conclusively, the traffic flow models generated using the radar data seems to be more realistic.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.4
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• pp.20-29
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• 2014

Density is applied both three major macroscopic traffic variables (traffic volume, speed, and density) and two measures of effectiveness (MOE) for level of service (LOS) on highway (density and V/C). Especially, it is known for the most accurate MOE on evaluating the LOS of highway. Despite such importance, there is a lack of study on density relatively than other variables for its difficulty of measurement. Existing density estimation methods have some limitations such as density values of same traffic flow vary with collecting time. In this study, we researched actual density measuring method with panoramic image, after each CCTV images in the Sapaesan Tunnel on Seoul Ring Expressway are matched into one panoramic image. Analysis through the Central Limit Theorem shows that density of 24 1 km-images, which means 24 second, applies traffic situation well. That is to say that reasonable density value regardless of collecting time, and practical density which represents actual traffic flow can be taken in case of measuring density by suggested collecting cycle.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.4D
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• pp.443-450
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• 2009

Uninterrupted facility - since there is a close relationship between traffic volume, speed and density -, when a ramp traffic flow merges into the main line, will change the traffic speed or density, and the corresponding correlational model equation will be changed. Thus, this study, using time and space-series traffic data on areas under the influence of such a merging, identified sections which changed the correlation between speed and density variables, and examined such changes. As a result, the upstream and merging sections showed the "Underwood"-shaped exponent, and the downstream after passing the merging section showed a straight line "Greenshields" model. The downstream section which changed the correlation between speed and density showed a gradual downstream movement phenomenon within 100 m-500 m from the end of the third lane linking with the ramp, as the traffic approached the inner lanes. Also, the upstream section, merging section, and downstream section involving a change showed heterogeneous traffic flows which, in the speed-density model, have a statistically different free flow speed (constant) and a different ratio of free flow speed to jam density (gradient).

• Journal of Korean Society of Transportation
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• v.6 no.1
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• pp.43-53
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• 1988

This study is to find the proper method of estimating urban road traffic capacity. To estimate road traffic capacity, the following methods are chosen ; 1) crossing point of Q-V and S-V, 2) critical velocity and density of Q-V-K model, 3) V-K model with density parameter. The density estimated through S-V relation is 174 veh./km. The methods used in this paper yields more stable values with 2286 veh./h/ in average. The estimated average capacity by three methods are 2272 veh./h. in multilane road. 2411 veh./h in three lane road and 2185 veh./h. in two lane road.